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Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time
Joint Authors
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-19
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
With the rapid evolution of high-throughput technologies, time series/longitudinal high-throughput experiments have become possible and affordable.
However, the development of statistical methods dealing with gene expression profiles across time points has not kept up with the explosion of such data.
The feature selection process is of critical importance for longitudinal microarray data.
In this study, we proposed aggregating a gene’s expression values across time into a single value using the sign average method, thereby degrading a longitudinal feature selection process into a classic one.
Regularized logistic regression models with pseudogenes (i.e., the sign average of genes across time as predictors) were then optimized by either the coordinate descent method or the threshold gradient descent regularization method.
By applying the proposed methods to simulated data and a traumatic injury dataset, we have demonstrated that the proposed methods, especially for the combination of sign average and threshold gradient descent regularization, outperform other competitive algorithms.
To conclude, the proposed methods are highly recommended for studies with the objective of carrying out feature selection for longitudinal gene expression data.
American Psychological Association (APA)
Tian, Suyan& Wang, Chi. 2019. Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time. BioMed Research International،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1123479
Modern Language Association (MLA)
Tian, Suyan& Wang, Chi. Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time. BioMed Research International No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1123479
American Medical Association (AMA)
Tian, Suyan& Wang, Chi. Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1123479
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1123479